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PreprintCC BYChemRxiv

Additive Manufacturing of Polyvinylidene Fluoride (PVDF) Toward High Performance with AI-Driven Workflows

  • Rigoberto C. Advincula1,2,3,4,5,6
  • John Ryan C. Dizon7,8
  • Tabitha D. Burch1,2
  • Xiang Cheng5,6
  • Alejandro H. Espera1,2
  • Erick L. Ribeiro1,2
  • Eugene B. Caldona9,10
  • H. Felix Wu11
  • Yijie Jiang12,13
  • Wonbong Choi14,15
15 affiliations
  1. 1Department of Chemical and Biomolecular Engineering, Department of Materials Science and Engineering, and Institute for Advanced Materials and Manufacturing
  2. 2University of Tennessee
  3. 3Center for Nanophase Materials and Sciences
  4. 4Oak Ridge National Laboratory
  5. 5Department of Macromolecular Sciences and Engineering
  6. 6Case Western Reserve University
  7. 7DR3AM Center and Department of Industrial Engineering, College of Engineering and Architecture
  8. 8Bataan Peninsula State University
  9. 9Department of Coatings and Polymeric Materials
  10. 10North Dakota State University
  11. 11Office of Critical Minerals and Energy Innovation (CMEI, U.S. Department of Energy, 1000 Independence Avenue Southwest, Washington, District of Columbia, 20585
  12. 12School of Aerospace & Mechanical Engineering
  13. 13University of Oklahoma
  14. 14Department of Materials Science and Engineering
  15. 15University of North Texas
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Abstract

Polyvinylidene fluoride (PVDF) is a high-performance fluoropolymer known for its chemical resistance, mechanical strength, thermal stability, and piezoelectric and dielectric behavior, making it an ideal material for additive manufacturing (AM). The first part of this paper provides an overview and demonstration of this potential, with the first section reviewing PVDF materials, crystallographic phases, and fused deposition modeling (FDM) processing. This perspective provides practical guidance for optimizing PVDF AM for engineering applications, as well as the potential for artificial intelligence and machine learning (AI/ML) workflows to inform future research.

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Cite

BibTeXRIS
BibTeX
@misc{advincula2026additive,
  title = {{Additive Manufacturing of Polyvinylidene Fluoride (PVDF) Toward High Performance with AI-Driven Workflows}},
  author = {Rigoberto C. Advincula and John Ryan C. Dizon and Tabitha D. Burch and Xiang Cheng and Alejandro H. Espera and Erick L. Ribeiro and Eugene B. Caldona and H. Felix Wu and Yijie Jiang and Wonbong Choi},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15009888/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009888/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Additive Manufacturing of Polyvinylidene Fluoride (PVDF) Toward High Performance with AI-Driven Workflows
AU  - Rigoberto C. Advincula
AU  - John Ryan C. Dizon
AU  - Tabitha D. Burch
AU  - Xiang Cheng
AU  - Alejandro H. Espera
AU  - Erick L. Ribeiro
AU  - Eugene B. Caldona
AU  - H. Felix Wu
AU  - Yijie Jiang
AU  - Wonbong Choi
PY  - 2026
DA  - 2026/10/05
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009888/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009888/v1
AB  - Polyvinylidene fluoride (PVDF) is a high-performance fluoropolymer known for its chemical resistance, mechanical strength, thermal stability, and piezoelectric and dielectric behavior, making it an ideal material for additive manufacturing (AM). The first part of this paper provides an overview and demonstration of this potential, with the first section reviewing PVDF materials, crystallographic phases, and fused deposition modeling (FDM) processing. This perspective provides practical guidance for optimizing PVDF AM for engineering applications, as well as the potential for artificial intelligence and machine learning (AI/ML) workflows to inform future research.
ER  -